João Sedoc

1.7k total citations · 1 hit paper
56 papers, 634 citations indexed

About

João Sedoc is a scholar working on Artificial Intelligence, Social Psychology and Sociology and Political Science. According to data from OpenAlex, João Sedoc has authored 56 papers receiving a total of 634 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 10 papers in Social Psychology and 10 papers in Sociology and Political Science. Recurrent topics in João Sedoc's work include Topic Modeling (29 papers), Natural Language Processing Techniques (19 papers) and Mental Health via Writing (9 papers). João Sedoc is often cited by papers focused on Topic Modeling (29 papers), Natural Language Processing Techniques (19 papers) and Mental Health via Writing (9 papers). João Sedoc collaborates with scholars based in United States, Germany and South Korea. João Sedoc's co-authors include Lyle Ungar, Smisha Agarwal, Sven Buechel, Johannes C. Eichstaedt, H. Andrew Schwartz, Shannon Wiltsey Stirman, David B. Yaden, Robert J. DeRubeis, Robb Willer and Elizabeth Cameron Stade and has published in prestigious journals such as SHILAP Revista de lepidopterología, Biological Psychiatry and IEEE Access.

In The Last Decade

João Sedoc

52 papers receiving 610 citations

Hit Papers

Large language models could change the future of behavior... 2024 2026 2025 2024 25 50 75 100
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation
    2024 110 citations Elizabeth Cameron Stade, Shannon Wiltsey Stirman et al. SHILAP Revista de lepidopterología profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
João Sedoc United States 12 306 119 109 71 60 56 634
Ha Trinh United States 10 214 0.7× 121 1.0× 133 1.2× 53 0.7× 45 0.8× 27 441
Marcos Báez Italy 13 227 0.7× 69 0.6× 66 0.6× 16 0.2× 86 1.4× 49 735
Kael Rowan United States 15 131 0.4× 127 1.1× 230 2.1× 138 1.9× 100 1.7× 24 686
Rabia Bashir Australia 7 370 1.2× 143 1.2× 343 3.1× 81 1.1× 73 1.2× 15 896
Manas Gaur United States 17 390 1.3× 246 2.1× 139 1.3× 72 1.0× 65 1.1× 54 721
Coosje Lisabet Sterre Veldkamp Netherlands 10 88 0.3× 90 0.8× 59 0.5× 140 2.0× 104 1.7× 13 850
Juan Martı́nez-Miranda Mexico 14 167 0.5× 157 1.3× 131 1.2× 99 1.4× 76 1.3× 44 580
Benjamin Erb Germany 10 122 0.4× 76 0.6× 158 1.4× 83 1.2× 153 2.5× 31 524
Jichen Zhu United States 15 452 1.5× 119 1.0× 59 0.5× 25 0.4× 182 3.0× 74 804
Paweł Matykiewicz United States 12 507 1.7× 288 2.4× 85 0.8× 88 1.2× 30 0.5× 18 820

Countries citing papers authored by João Sedoc

Since Specialization
Citations

This map shows the geographic impact of João Sedoc's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by João Sedoc with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites João Sedoc more than expected).

Fields of papers citing papers by João Sedoc

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by João Sedoc. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by João Sedoc. The network helps show where João Sedoc may publish in the future.

Co-authorship network of co-authors of João Sedoc

This figure shows the co-authorship network connecting the top 25 collaborators of João Sedoc. A scholar is included among the top collaborators of João Sedoc based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with João Sedoc. João Sedoc is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Giorgi, Salvatore, et al.. (2024). Findings of WASSA 2024 Shared Task on Empathy and Personality Detection in Interactions. 369–379. 1 indexed citations
2.
Ireland, Molly, et al.. (2024). Large language models display human-like social desirability biases in Big Five personality surveys. PNAS Nexus. 3(12). pgae533–pgae533. 8 indexed citations
3.
Sedoc, João, et al.. (2024). Usability, Engagement, and Report Usefulness of Chatbot-Based Family Health History Data Collection: Mixed Methods Analysis. Journal of Medical Internet Research. 26. e55164–e55164. 2 indexed citations
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Schwartz, H. Andrew, et al.. (2024). Large Human Language Models: A Need and the Challenges. 8631–8646. 3 indexed citations
6.
Nowak, Agnieszka, Leonardo F. R. Ribeiro, João Sedoc, et al.. (2024). On the Role of Summary Content Units in Text Summarization Evaluation. 272–281.
7.
Stade, Elizabeth Cameron, Shannon Wiltsey Stirman, Lyle Ungar, et al.. (2024). Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation. SHILAP Revista de lepidopterología. 3(1). 12–12. 110 indexed citations breakdown →
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Ungar, Lyle, et al.. (2023). Conditioning on Dialog Acts improves Empathy Style Transfer. 13254–13271. 3 indexed citations
11.
Gretz, Shai, Assaf Toledo, Dan Lahav, et al.. (2023). Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy. 1358–1370. 1 indexed citations
12.
Li, Yifei, Lyle Ungar, & João Sedoc. (2023). Conceptor-Aided Debiasing of Large Language Models. 10703–10727. 2 indexed citations
13.
Ungar, Lyle, et al.. (2023). An “Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives”. PubMed. 2023. 11346–11369. 7 indexed citations
14.
Sedoc, João, Assaf Toledo, Shai Gretz, et al.. (2022). Chatbot-Delivered COVID-19 Vaccine Communication Message Preferences of Young Adults and Public Health Workers in Urban American Communities: Qualitative Study. Journal of Medical Internet Research. 24(7). e38418–e38418. 18 indexed citations
15.
Sedoc, João, Shai Gretz, Assaf Toledo, et al.. (2022). Usability and Credibility of a COVID-19 Vaccine Chatbot for Young Adults and Health Workers in the United States: Formative Mixed Methods Study. JMIR Human Factors. 10. e40533–e40533. 17 indexed citations
16.
Sedoc, João, et al.. (2022). Health-focused conversational agents in person-centered care: a review of apps. npj Digital Medicine. 5(1). 21–21. 65 indexed citations
17.
Tang, Sunny X., Sunghye Cho, Raquel E. Gur, et al.. (2021). Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders. Schizophrenia. 7(1). 25–25. 75 indexed citations
18.
Poliak, Adam, Cash Costello, Kenton Murray, et al.. (2020). Collecting Verified COVID-19 Question Answer Pairs. 8 indexed citations
19.
Xia, Patrick, João Sedoc, & Benjamin Van Durme. (2020). Revisiting Memory-Efficient Incremental Coreference Resolution.. arXiv (Cornell University). 1 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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